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MM and NHL Trends

Andrew Portuguese <<<<<<< HEAD
January 26, 2024 =======
January 24, 2024 >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622

SETUP & DATA WRANGLING

Open libraries

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Read in data

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db = read_excel(path="keep11_2.xlsx")

insurance = read_excel(path="inscvgpr.xlsx")

db <- db %>%
  mutate(
    rxdate = ymd(rxdate),
    Tx.year = year(rxdate),
    Fiscal.year = case_when(
      rxdate >= ymd("2017-07-01") & rxdate <= ymd("2018-06-30") ~ 2018,
      rxdate >=ymd("2018-07-01") & rxdate <= ymd("2019-06-30") ~ 2019,
      rxdate >=ymd("2019-07-01") & rxdate <= ymd("2020-06-30") ~ 2020,
      rxdate >=ymd("2020-07-01") & rxdate <= ymd("2021-06-30") ~ 2021,
      rxdate >=ymd("2021-07-01") & rxdate <= ymd("2022-06-30") ~ 2022,
      rxdate >=ymd("2022-07-01") & rxdate <= ymd("2023-06-30") ~ 2023,
      rxdate >=ymd("2022-07-01") & rxdate <= ymd("2024-06-30") ~ 2024
    ),
    Distance.to.FH = zip_distance("98115",zipcode, lonlat = TRUE, units = "miles")$distance,
    tx = as.integer(tx)
  )

Zip.df <- reverse_zipcode(db$zipcode) %>% unique() # Read in using reverse_zipcode function in zipcodeR package


db <- 
  left_join(
    by = "zipcode",
    x = db,
    y = Zip.df %>% select(zipcode, median_household_income, median_home_value, population, population_density)
  )

db <-
  left_join(
    by = "upn",
    x = db,
    y = insurance %>% select(PAYOR_NAME, FINANCIAL_CLASS, upn)
  )

CHANGES OVER TIME

Number of procedures (i.e., auto, allo, Abecma, Carvykti, Breyanzi, Yescarta)

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db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  count(Fiscal.year) %>%
  ggplot + aes(Fiscal.year, n) + 
  geom_point(stat="identity") + 
  geom_line(stat="identity", color="blue") + 
  ylab("Total number of procedures") + 
  xlab(NULL) +
  geom_text(aes(label=n), position=position_stack(vjust=1.05)) + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c( 2018,2019, 2020, 2021, 2022,2023)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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Plasma cell disorders: Bar graph of auto, allo, Abecma, and Carvykti

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## Counts
db %>%
  filter(dx.category == "MM") %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  count(rxtype, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    total = sum(n),
    pct = prop.table(n) * 100,
    Procedure = factor(rxtype, levels = c("Abecma","Carvykti", "Allo", "Auto"))
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Procedure) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) + 
  geom_text( aes(label=total, x = Fiscal.year, y = total + 7, vjust = 0), color = "#104a8e") +
  geom_smooth(aes(x = Fiscal.year, y=total), method = "lm", se = FALSE, color = "#104a8e", inherit.aes = FALSE, linetype = "dashed", size = 0.5)+
  ggtitle("Distribution of procedures - Multiple Myeloma") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(20, 40, 60, 80, 100, 120, 140, 160, 180)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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Lymphomas: Bar graph of auto, allo, Breyanzi, and Yescarta

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## Counts
db %>%
  filter(dx.category == "NHL") %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  count(rxtype, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    total = sum(n),
    pct = prop.table(n) * 100,
    Procedure = factor(rxtype, levels = c("Breyanzi","Yescarta", "Allo", "Auto"))
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Procedure) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) + 
  geom_text( aes(label=total, x = Fiscal.year, y = total + 7, vjust = 0), color = "#104a8e") +
  geom_smooth(aes(x = Fiscal.year, y=total), method = "lm", se = FALSE, color = "#104a8e", inherit.aes = FALSE, linetype = "dashed", size = 0.5)+
  ggtitle("Distribution of procedures - Lymphoma") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(20, 40, 60, 80, 100)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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NHL and MM: transplant vs CAR-T

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## Counts
db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  mutate(
    Procedure = case_when(
      rxtype == "Auto" ~ "Transplant",
      rxtype == "Allo" ~ "Transplant",
      rxtype == "Carvykti" ~ "CAR-T",
      rxtype == "Abecma" ~ "CAR-T",
      rxtype == "Yescarta" ~ "CAR-T",
      rxtype == "Breyanzi" ~ "CAR-T"
    )
  ) %>%
  count(Procedure, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    total = sum(n),
    pct = prop.table(n) * 100,
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Procedure) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) + 
  geom_text( aes(label=total, x = Fiscal.year, y = total + 7, vjust = 0), color = "#104a8e") +
  ggtitle("CAR-T vs Transplant") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(50, 100, 150, 200, 250, 300)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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Bar graph of 1st vs 2nd ASCTs

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## Counts
db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  filter(dx.category == "MM") %>%
  filter(rxtype == "Auto") %>%
  count(rxnum, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    ASCT = factor(recode(rxnum, "1" = "1st", "2" = "2nd", "3" = "3rd"), levels = c("3rd", "2nd", "1st") )
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = ASCT) + 
  geom_bar(stat="identity") + 
  ylab("Number of ASCTs") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) + 
  ggtitle("1st vs 2nd ASCT") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(20, 40, 60, 80, 100, 120, 140, 160, 180)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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Bar graph of inpatient vs outpatient ASCTs

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## Counts
db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  filter(rxtype == "Auto" & dx.category == "MM") %>%
  count(inpt, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Setting = factor(ifelse(inpt=="Y","Elective admission","Outpatient"), levels = c("Outpatient","Elective admission"))
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Setting) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n)), position=position_stack(vjust=0.5)) + 
  ggtitle("Inpatient vs outpatient ASCT (n)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(20, 40, 60, 80, 100, 120, 140)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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## Proportion
db %>%
  filter(rxtype == "Auto" & dx.category == "MM") %>%
  count(inpt, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Setting = factor(ifelse(inpt=="Y","Elective admission","Outpatient"), levels = c("Outpatient","Elective admission"))
    ) %>%
  ggplot + aes(Fiscal.year, pct, fill = Setting) + 
  geom_bar(stat="identity") + 
  ylab("Proportion of patients (%)") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(sprintf("%1.1f", pct),"%")), position=position_stack(vjust=0.5)) + 
  ggtitle("Inpatient vs outpatient ASCT (%)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
  scale_y_continuous(breaks = c(20, 40, 60, 80, 100)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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Bar graph of total inpatient duration

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db %>%
  group_by(Fiscal.year) %>%
  summarise(Inpatient.duration = median(inday, na.rm=TRUE), sd = sd(inday, na.rm = TRUE), n=n() ) %>%
  ggplot + aes(Fiscal.year, Inpatient.duration) + 
  geom_bar(stat="identity") + 
  geom_errorbar(aes(x=Fiscal.year, ymin = Inpatient.duration- (sd/sqrt(n) ), ymax=Inpatient.duration+(sd/sqrt(n) ) ), width=0.4, color = "black", alpha=0.9) +
  ylab("Inpatient duration, days (median)") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(Inpatient.duration)), position=position_stack(vjust=0.5), color = "white") + 
  ggtitle("Total inpatient duration") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
  scale_y_continuous(breaks = c(2, 4, 6, 8, 10, 12, 14) ) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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Bar graph of distance to FH

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db %>%
  group_by(Fiscal.year) %>%
  summarise(Distance = median(Distance.to.FH, na.rm = TRUE), sd = sd(Distance.to.FH, na.rm = TRUE), n=n()) %>%
  ggplot + aes(Fiscal.year, Distance) + 
  geom_bar(stat="identity") + 
  #geom_errorbar(aes(x=Fiscal.year, ymin = Distance- (sd/sqrt(n) ), ymax=Distance+(sd/sqrt(n) ) ), width=0.4, color = "orange", alpha=0.9) +
  ylab("Distance to FHCC, miles (median)") + 
  xlab(NULL) +
  geom_text(aes(label=signif(Distance,3)), position=position_stack(vjust=0.5), color = "white") + 
  ggtitle("Distance to FHCC") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
  #scale_y_continuous(breaks = c(2, 4, 6, 8, 10, 12, 14) ) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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Bar graph of ages

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## Counts
db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  mutate(
    Age.category = case_when(
      rxage <50 ~ "<50",
      rxage >=50 & rxage <55 ~ "50-55",
      rxage >=55 & rxage <60 ~ "55-60",
      rxage >=60 & rxage <65 ~ "60-65",
      rxage >=65 ~ "\u226565",
      .default = "other"
    )
  ) %>%
  count(Age.category, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Age = factor(Age.category, levels = c("<50", "50-55","55-60","60-65","\u226565") )
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Age) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) + 
  ggtitle("Distribution of age (n)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(50, 100, 150, 200, 250, 300, 350, 400)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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## Proportion
db %>%
  mutate(
    Age.category = case_when(
      rxage <50 ~ "<50",
      rxage >=50 & rxage <55 ~ "50-55",
      rxage >=55 & rxage <60 ~ "55-60",
      rxage >=60 & rxage <65 ~ "60-65",
      rxage >=65 ~ "\u226565",
      .default = "other"
    )
  ) %>%
  count(Age.category, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Age = factor(Age.category, levels = c("<50", "50-55","55-60","60-65","\u226565") )
    ) %>%
  ggplot + aes(Fiscal.year, pct, fill = Age) + 
  geom_bar(stat="identity") + 
  ylab("Proportion of patients (%)") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(sprintf("%1.1f", pct),"%")), position=position_stack(vjust=0.5)) + 
  ggtitle("Distribution of age (%)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
  scale_y_continuous(breaks = c(20,40,60,80,100)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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Bar graph of ages, filtered on MM

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## Counts
db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  filter(dx.category == "MM") %>%
  mutate(
    Age.category = case_when(
      rxage <50 ~ "<50",
      rxage >=50 & rxage <55 ~ "50-55",
      rxage >=55 & rxage <60 ~ "55-60",
      rxage >=60 & rxage <65 ~ "60-65",
      rxage >=65 ~ "\u226565",
      .default = "other"
    )
  ) %>%
  count(Age.category, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Age = factor(Age.category, levels = c("<50", "50-55","55-60","60-65","\u226565") )
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Age) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) + 
  ggtitle("Distribution of age (n)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
  scale_y_continuous(breaks = c(50, 100, 150, 200, 250, 300, 350, 400)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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## Proportion
db %>%
  filter(dx.category == "MM") %>%
  mutate(
    Age.category = case_when(
      rxage <50 ~ "<50",
      rxage >=50 & rxage <55 ~ "50-55",
      rxage >=55 & rxage <60 ~ "55-60",
      rxage >=60 & rxage <65 ~ "60-65",
      rxage >=65 ~ "\u226565",
      .default = "other"
    )
  ) %>%
  count(Age.category, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Age = factor(Age.category, levels = c("<50", "50-55","55-60","60-65","\u226565") )
    ) %>%
  ggplot + aes(Fiscal.year, pct, fill = Age) + 
  geom_bar(stat="identity") + 
  ylab("Proportion of patients (%)") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(sprintf("%1.1f", pct),"%")), position=position_stack(vjust=0.5)) + 
  ggtitle("Distribution of age (%)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(20,40,60,80,100)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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Bar graph of sex

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## Counts
db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  count(sex, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Sex = sex
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Sex) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n,sprintf("\n(%1.1f", pct),"%)")), position=position_stack(vjust=0.5)) + 
  ggtitle("Distribution of sex (n)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(20,40,60,80,100, 120, 140, 160)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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## Proportion
db %>%
  count(sex, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Sex = sex
    ) %>%
  ggplot + aes(Fiscal.year, pct, fill = Sex) + 
  geom_bar(stat="identity") + 
  ylab("Proportion of patients (%)") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n,sprintf("\n(%1.1f", pct),"%)")), position=position_stack(vjust=0.5)) + 
  ggtitle("Distribution of sex (%)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
  scale_y_continuous(breaks = c(20,40,60,80,100)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
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TABLES OF REFERRALS AND INSURANCE

Table of referring facilities

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theme_gtsummary_compact()

db %>%
  transmute(
    "Rx year" = Fiscal.year,
    "Referring facility" = referring.facility
  ) %>%
  tbl_summary(
    by = "Rx year",
    missing = "ifany",
    sort = all_categorical() ~ "frequency",
    statistic = list(
      all_continuous() ~ "{median} ({p25}, {p75})",
      all_categorical() ~ "{n} ({p}%)"
    )
  ) %>%
  bold_labels()
<<<<<<< HEAD
<<<<<<< HEAD ======= >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622 <<<<<<< HEAD ======= >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622 <<<<<<< HEAD ======= >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622 <<<<<<< HEAD ======= >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622 <<<<<<< HEAD =======
Characteristic2018, N = 831 2019, N = 2221 2020, N = 2061 2021, N = 2231 2022, N = 2831 2023, N = 3211 2024, N = 13312018, N = 1921 2019, N = 2281 2020, N = 1981 2021, N = 2551 2022, N = 2901 2023, N = 3081
Referring facility






    Internal22 (39%) 39 (24%) 35 (22%) 35 (21%) 56 (23%) 67 (24%) 35 (35%)
    Other 11 (19%) 29 (18%) 26 (16%) 29 (18%) 34 (14%) 40 (14%) 9 (8.9%)
    Multicare 2 (3.5%) 6 (3.7%) 16 (10%) 6 (3.6%) 14 (5.9%) 23 (8.1%) 5 (5.0%)
    Northwest Medical Specialties 1 (1.8%) 9 (5.6%) 14 (8.9%) 4 (2.4%) 11 (4.6%) 14 (4.9%) 7 (6.9%)
    Providence 2 (3.5%) 4 (2.5%) 6 (3.8%) 8 (4.8%) 14 (5.9%) 22 (7.7%) 4 (4.0%)
    Valley Med Center 4 (7.0%) 8 (4.9%) 5 (3.2%) 6 (3.6%) 5 (2.1%) 13 (4.6%) 6 (5.9%)
    Swedish 2 (3.5%) 7 (4.3%) 4 (2.5%) 3 (1.8%) 11 (4.6%) 4 (1.4%) 1 (1.0%)
    Confluence Health 1 (1.8%) 8 (4.9%) 3 (1.9%) 5 (3.0%) 7 (2.9%) 4 (1.4%) 2 (2.0%)
    Kadlec Clinic 1 (1.8%) 3 (1.9%) 5 (3.2%) 8 (4.8%) 6 (2.5%) 3 (1.1%) 4 (4.0%)
    Virginia Mason 1 (1.8%) 2 (1.2%) 1 (0.6%) 7 (4.2%) 11 (4.6%) 6 (2.1%) 1 (1.0%)
    Cancer Care Northwest 1 (1.8%) 2 (1.2%) 2 (1.3%) 1 (0.6%) 11 (4.6%) 10 (3.5%) 1 (1.0%)
    Alaska Oncology & Hematology 0 (0%) 2 (1.2%) 0 (0%) 11 (6.7%) 3 (1.3%) 8 (2.8%) 1 (1.0%)
    Peace Health 0 (0%) 2 (1.2%) 4 (2.5%) 6 (3.6%) 4 (1.7%) 8 (2.8%) 1 (1.0%)
    Everett Clinic 0 (0%) 0 (0%) 8 (5.1%) 3 (1.8%) 5 (2.1%) 7 (2.5%) 1 (1.0%)
    Skagit 1 (1.8%) 2 (1.2%) 4 (2.5%) 2 (1.2%) 6 (2.5%) 8 (2.8%) 1 (1.0%)
    Kootenai Cancer Center 0 (0%) 1 (0.6%) 5 (3.2%) 7 (4.2%) 4 (1.7%) 2 (0.7%) 3 (3.0%)
    Straub Clinic and Hospital 0 (0%) 4 (2.5%) 5 (3.2%) 1 (0.6%) 6 (2.5%) 5 (1.8%) 1 (1.0%)
    Vista Oncology 0 (0%) 5 (3.1%) 0 (0%) 4 (2.4%) 0 (0%) 4 (1.4%) 3 (3.0%)
    NW Allergy & Asthma 0 (0%) 2 (1.2%) 1 (0.6%) 2 (1.2%) 7 (2.9%) 2 (0.7%) 1 (1.0%)
    Kaiser 1 (1.8%) 3 (1.9%) 0 (0%) 5 (3.0%) 2 (0.8%) 1 (0.4%) 2 (2.0%)
    Katmai Oncology Group 2 (3.5%) 3 (1.9%) 1 (0.6%) 1 (0.6%) 0 (0%) 3 (1.1%) 4 (4.0%)
    North Star Lodge 1 (1.8%) 4 (2.5%) 2 (1.3%) 2 (1.2%) 2 (0.8%) 2 (0.7%) 1 (1.0%)
    UWNC 0 (0%) 2 (1.2%) 2 (1.3%) 2 (1.2%) 3 (1.3%) 2 (0.7%) 2 (2.0%)
    Alaska Native Medical Center 1 (1.8%) 0 (0%) 1 (0.6%) 3 (1.8%) 3 (1.3%) 3 (1.1%) 0 (0%)
    Madigan 1 (1.8%) 4 (2.5%) 0 (0%) 0 (0%) 5 (2.1%) 1 (0.4%) 0 (0%)
    Jefferson Healthcare Oncology Clini 0 (0%) 2 (1.2%) 1 (0.6%) 1 (0.6%) 2 (0.8%) 3 (1.1%) 0 (0%)
    Queen's cancer center 0 (0%) 2 (1.2%) 1 (0.6%) 0 (0%)37 (28%) 47 (27%) 29 (19%) 47 (23%) 55 (22%) 74 (29%)
    Other 25 (19%) 32 (19%) 22 (15%) 35 (17%) 38 (15%) 26 (10%)
    Multicare 4 (3.0%) 11 (6.4%) 12 (8.1%) 7 (3.4%) 17 (6.7%) 21 (8.3%)
    Northwest Medical Specialties 5 (3.8%) 9 (5.2%) 13 (8.7%) 7 (3.4%) 12 (4.7%) 14 (5.5%)
    Providence 4 (3.0%) 4 (2.3%) 7 (4.7%) 16 (7.8%) 15 (5.9%) 14 (5.5%)
    Valley Med Center 5 (3.8%) 10 (5.8%) 5 (3.4%) 5 (2.4%) 11 (4.3%) 11 (4.3%)
    Swedish 7 (5.3%) 4 (2.3%) 2 (1.3%) 8 (3.9%) 9 (3.5%) 2 (0.8%)
    Confluence Health 5 (3.8%) 6 (3.5%) 2 (1.3%) 7 (3.4%) 5 (2.0%) 5 (2.0%)
    Kadlec Clinic 3 (2.3%) 4 (2.3%) 10 (6.7%) 2 (1.0%) 5 (2.0%) 6 (2.4%)
    Virginia Mason 1 (0.8%) 3 (1.7%) 2 (1.3%) 9 (4.4%) 9 (3.5%) 5 (2.0%)
    Cancer Care Northwest 2 (1.5%) 2 (1.2%) 1 (0.7%) 3 (1.5%) 16 (6.3%) 4 (1.6%)
    Alaska Oncology & Hematology 0 (0%) 2 (1.2%) 3 (2.0%) 9 (4.4%) 7 (2.7%) 4 (1.6%)
    Peace Health 2 (1.5%) 1 (0.6%) 8 (5.4%) 4 (2.0%) 4 (1.6%) 6 (2.4%)
    Everett Clinic 0 (0%) 4 (2.3%) 5 (3.4%) 2 (1.0%) 11 (4.3%) 2 (0.8%)
    Skagit 2 (1.5%) 1 (0.6%) 4 (2.7%) 5 (2.4%) 5 (2.0%) 7 (2.8%)
    Kootenai Cancer Center 0 (0%) 4 (2.3%) 6 (4.0%) 4 (2.0%) 3 (1.2%) 5 (2.0%)
    Straub Clinic and Hospital 0 (0%) 7 (4.1%) 2 (1.3%) 4 (2.0%) 5 (2.0%) 4 (1.6%)
    Vista Oncology 3 (2.3%) 2 (1.2%) 3 (2.0%) 1 (0.5%) 1 (0.4%) 6 (2.4%)
    NW Allergy & Asthma 0 (0%) 3 (1.7%) 1 (0.7%) 7 (3.4%) 1 (0.4%) 3 (1.2%)
    Kaiser 4 (3.0%) 0 (0%) 0 (0%) 6 (2.9%) 2 (0.8%) 2 (0.8%)
    Katmai Oncology Group 3 (2.3%) 3 (1.7%) 1 (0.7%) 0 (0%) 0 (0%) 7 (2.8%)
    North Star Lodge 5 (3.8%) 1 (0.6%) 2 (1.3%) 2 (1.0%) 1 (0.4%) 3 (1.2%)
    UWNC 1 (0.8%) 2 (1.2%) 3 (2.0%) 1 (0.5%) 4 (1.6%) 2 (0.8%)
    Alaska Native Medical Center 1 (0.8%) 0 (0%) 2 (1.3%) 4 (2.0%) 3 (1.2%) 1 (0.4%)
    Madigan 4 (3.0%) 1 (0.6%) 0 (0%) 3 (1.5%) 3 (1.2%) 0 (0%)
    Jefferson Healthcare Oncology Clini 1 (0.8%) 1 (0.6%) 2 (1.3%) 0 (0%) 4 (1.6%) 1 (0.4%)
    Queen's cancer center 2 (1.5%) 1 (0.6%) 0 (0%) 0 (0%) 1 (0.4%) 5 (2.0%)
    St Michael 3 (2.3%) 3 (1.7%) 0 (0%) 1 (0.5%) 0 (0%) 2 (0.8%)
    VA 1 (0.8%) 0 (0%) 0 (0%) 2 (1.0%) 1 (0.4%) 4 (1.6%)
    Rockwood Cancer and Blood Specialty 1 (0.8%) 3 (1.7%) 1 (0.7%) 1 (0.5%)0 (0%) 3 (1.1%) 3 (3.0%)
    St Michael 1 (1.8%) 3 (1.9%) 2 (1.3%) 0 (0%) 1 (0.4%) 2 (0.7%) 0 (0%)
    VA 0 (0%) 1 (0.6%) 0 (0%) 1 (0.6%) 1 (0.4%) 4 (1.4%) 1 (1.0%)
    Rockwood Cancer and Blood Specialty 0 (0%) 2 (1.2%) 2 (1.3%) 1 (0.6%) 1 (0.4%) 0 (0%) 1 (1.0%)
    Logan Health Hematology & Oncology 0 (0%) 0 (0%)1 (0.6%) 0 (0%) 0 (0%) 5 (1.8%) 0 (0%)
    Northwest Oncology and Hematology 1 (1.8%) 1 (0.6%) 1 (0.6%) 0 (0%) 2 (0.8%) 1 (0.4%) 0 (0%)
1 (0.7%) 0 (0%) 3 (1.2%) 2 (0.8%)
    Northwest Oncology and Hematology 2 (1.5%) 1 (0.6%) 0 (0%) 2 (1.0%) 1 (0.4%) 0 (0%)
    Compass Oncology 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)4 (1.4%) 0 (0%)
4 (1.6%)
    Hawaii Cancer Care Waterfront Plaza 0 (0%) 0 (0%) 0 (0%)1 (0.6%) 2 (0.8%) 1 (0.4%) 0 (0%)
    Unknown 26 60 48 58 44 36 32
1 (0.5%) 3 (1.2%) 0 (0%)
    Unknown 59 56 49 50 35 55
1 n (%)

Table of insurance companies

Show code
theme_gtsummary_compact()

db %>%
  transmute(
    "Rx year" = Tx.year,
    "Insurance company" = PAYOR_NAME,
    Insurance = FINANCIAL_CLASS
  ) %>%
  tbl_summary(
    by = "Rx year",
    missing = "ifany",
    sort = all_categorical() ~ "frequency",
    statistic = list(
      all_continuous() ~ "{median} ({p25}, {p75})",
      all_categorical() ~ "{n} ({p}%)"
    )
  )%>%
  bold_labels()
>>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622
Characteristic 2018, N = 1921 2019, N = 2281 2020, N = 1981 2021, N = 2551 2022, N = 2901 2023, N = 3081
Insurance company





    HB/PB BDCT 55 (29%) 80 (35%) 42 (21%) 57 (22%) 65 (22%) 78 (25%)
    MEDICARE 50 (26%) 33 (14%) 44 (22%) 62 (24%) 56 (19%) 64 (21%)
    HB/PB URN - OPTUM HEALTH 12 (6.3%) 18 (7.9%) 20 (10%) 18 (7.1%) 24 (8.3%) 17 (5.5%)
    MOLINA HEALTHCARE MEDICAID 9 (4.7%) 11 (4.8%) 17 (8.6%) 12 (4.7%) 15 (5.2%) 18 (5.8%)
    AETNA US HEALTHCARE 12 (6.3%) 11 (4.8%) 7 (3.5%) 14 (5.5%) 18 (6.2%) 14 (4.5%)
    PREMERA BLUE CROSS 9 (4.7%) 11 (4.8%) 2 (1.0%) 7 (2.8%) 26 (9.0%) 11 (3.6%)
    HEALTH NET FEDERAL SERVICES TRICARE 1 (0.5%) 3 (1.3%) 9 (4.5%) 11 (4.3%) 5 (1.7%) 4 (1.3%)
    WA MEDICAID 2 (1.0%) 5 (2.2%) 3 (1.5%) 7 (2.8%) 8 (2.8%) 5 (1.6%)
    KAISER FHP OF WA (GROUP HEALTH) 7 (3.6%) 3 (1.3%) 2 (1.0%) 3 (1.2%) 4 (1.4%) 9 (2.9%)
    BCBS OUT OF AREA 1 (0.5%) 3 (1.3%) 2 (1.0%) 5 (2.0%) 4 (1.4%) 10 (3.2%)
    GENERIC/UNLISTED 4 (2.1%) 8 (3.5%) 5 (2.5%) 1 (0.4%) 0 (0%) 2 (0.6%)
    HB/PB INTERLINK HEALTH SERVICES 4 (2.1%) 2 (0.9%) 1 (0.5%) 5 (2.0%) 1 (0.3%) 6 (1.9%)
    COMMUNITY HEALTH PLAN OF WA MEDICAID 3 (1.6%) 1 (0.4%) 5 (2.5%) 0 (0%) 4 (1.4%) 3 (1.0%)
    GRANTS AND BUDGETS 1 (0.5%) 3 (1.3%) 4 (2.0%) 7 (2.8%) 1 (0.3%) 0 (0%)
    REGENCE BLUE SHIELD 1 (0.5%) 2 (0.9%) 3 (1.5%) 2 (0.8%) 2 (0.7%) 6 (1.9%)
    COORDINATED CARE CORPORATION MEDICAID 3 (1.6%) 1 (0.4%) 2 (1.0%) 3 (1.2%) 3 (1.0%) 3 (1.0%)
    OPTUM HEALTHCARE SOLUTIONS/URN 2 (1.0%) 1 (0.4%) 2 (1.0%) 0 (0%) 4 (1.4%) 6 (1.9%)
    BCBS OF ILLINOIS - BOEING 1 (0.5%) 4 (1.8%) 0 (0%) 2 (0.8%) 2 (0.7%) 5 (1.6%)
    WELLPOINT MEDICAID 2 (1.0%) 5 (2.2%) 1 (0.5%) 0 (0%) 5 (1.7%) 1 (0.3%)
    ALASKA MEDICAID 1 (0.5%) 0 (0%) 2 (1.0%) 3 (1.2%) 2 (0.7%) 3 (1.0%)
    CIGNA 0 (0%) 2 (0.9%) 2 (1.0%) 5 (2.0%) 0 (0%) 2 (0.6%)
    TRIWEST HEALTHCARE ALLIANCE 1 (0.5%) 1 (0.4%) 0 (0%) 2 (0.8%) 1 (0.3%) 6 (1.9%)
    HUMANA MEDICARE 0 (0%) 0 (0%) 3 (1.5%) 2 (0.8%) 4 (1.4%) 1 (0.3%)
    AETNA MEDICARE 0 (0%) 1 (0.4%) 0 (0%) 1 (0.4%) 2 (0.7%) 5 (1.6%)
    HB/PB LIFETRAC 1 (0.5%) 2 (0.9%) 2 (1.0%) 2 (0.8%) 1 (0.3%) 1 (0.3%)
    REGENCE BLUE SHIELD MEDICARE 0 (0%) 1 (0.4%) 0 (0%) 3 (1.2%) 2 (0.7%) 3 (1.0%)
    HEALTHCARE MGMT ADMIN 0 (0%) 1 (0.4%) 1 (0.5%) 3 (1.2%) 2 (0.7%) 0 (0%)
    FHCC CONTRACTS 0 (0%) 2 (0.9%) 2 (1.0%) 1 (0.4%) 1 (0.3%) 0 (0%)
    KAISER FHP OF WA MEDICARE (GROUP HEALTH) 0 (0%) 1 (0.4%) 3 (1.5%) 0 (0%) 0 (0%) 2 (0.6%)
    MERITAIN HEALTH AETNA 1 (0.5%) 1 (0.4%) 0 (0%) 2 (0.8%) 2 (0.7%) 0 (0%)
    MOLINA HEALTHCARE 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (1.7%) 1 (0.3%)
    MONTANA MEDICAID 0 (0%) 1 (0.4%) 1 (0.5%) 0 (0%) 1 (0.3%) 3 (1.0%)
    REGENCE UMP 1 (0.5%) 0 (0%) 0 (0%) 1 (0.4%) 2 (0.7%) 2 (0.6%)
    UNITED HEALTHCARE MEDICAID 1 (0.5%) 0 (0%) 1 (0.5%) 1 (0.4%) 2 (0.7%) 1 (0.3%)
    ZENITH AMERICAN SOLUTIONS AETNA 0 (0%) 1 (0.4%) 2 (1.0%) 2 (0.8%) 0 (0%) 1 (0.3%)
    BCBS OUT OF AREA MEDICARE 0 (0%) 0 (0%) 2 (1.0%) 1 (0.4%) 2 (0.7%) 0 (0%)
    DEPT OF LABOR AND INDUSTRIES 0 (0%) 2 (0.9%) 0 (0%) 0 (0%) 0 (0%) 2 (0.6%)
    FEP 1 (0.5%) 0 (0%) 0 (0%) 0 (0%) 3 (1.0%) 0 (0%)
    KAISER FHP OF WA CORE-HIX (GROUP HEALTH) 1 (0.5%) 2 (0.9%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%)
    PREMERA BLUE CROSS MEDICARE 0 (0%) 0 (0%) 1 (0.5%) 2 (0.8%) 1 (0.3%) 0 (0%)
    US DEPARTMENT OF LABOR 1 (0.5%) 1 (0.4%) 0 (0%) 0 (0%) 0 (0%) 2 (0.6%)
    FINANCIAL ASSISTANCE 0 (0%) 0 (0%) 2 (1.0%) 0 (0%) 0 (0%) 1 (0.3%)
    MOLINA HEALTHCARE MEDICARE 0 (0%) 1 (0.4%) 0 (0%) 1 (0.4%) 0 (0%) 1 (0.3%)
    OPTUM CARE NETWORK MEDICARE 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 1 (0.3%) 1 (0.3%)
    ASURIS NORTHWEST HEALTH 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%) 1 (0.3%)
    BRITISH COLUMBIA 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 1 (0.3%)
    FHCC HOLDING FUNDS 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%) 1 (0.3%)
    IDAHO MEDICAID 0 (0%) 1 (0.4%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%)
    INTERLINK 0 (0%) 0 (0%) 1 (0.5%) 0 (0%) 0 (0%) 1 (0.3%)
    MEDICARE ADVANTAGE GENERIC 1 (0.5%) 1 (0.4%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    PACIFICSOURCE HEALTH PLANS 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 1 (0.3%)
    UNITED HEALTHCARE 0 (0%) 1 (0.4%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%)
    BCBS OUT OF AREA GENERIC 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%) 0 (0%)
    BCBS OUT OF AREA MEDICAID 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%) 0 (0%)
    COMMUNITY HEALTH PLAN OF WA MEDICARE 1 (0.5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    FIRST CHOICE 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%) 0 (0%)
    GEHA 1 (0.5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    ILWU BENEFIT PLANS 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%)
    LIFEWISE HEALTH PLAN OF WA 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%) 0 (0%)
    SELF INSURED WORKERS COMP OUT OF STATE 0 (0%) 0 (0%) 1 (0.5%) 0 (0%) 0 (0%) 0 (0%)
    SELF INSURED WORKERS COMP WA 0 (0%) 0 (0%) 1 (0.5%) 0 (0%) 0 (0%) 0 (0%)
    UNITED HEALTHCARE MEDICARE 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%)
    UNITED HEALTHCARE WEST MEDICARE 1 (0.5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    WA STATE HEALTH INS POOL-BMI210 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%)
    WELLCARE OUT OF STATE MEDICARE 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%) 0 (0%)
    WELLPOINT MEDICARE 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%) 0 (0%)
    Unknown 0 0 0 1 1 0
Insurance





    Case Rate 72 (38%) 102 (45%) 65 (33%) 82 (32%) 91 (31%) 102 (33%)
    Medicare 53 (28%) 38 (17%) 53 (27%) 74 (29%) 70 (24%) 77 (25%)
    Commercial 42 (22%) 51 (22%) 29 (15%) 51 (20%) 78 (27%) 75 (24%)
    Medicaid 21 (11%) 25 (11%) 32 (16%) 26 (10%) 41 (14%) 38 (12%)
    Other 2 (1.0%) 6 (2.6%) 6 (3.0%) 10 (3.9%) 4 (1.4%) 7 (2.3%)
    Tricare 1 (0.5%) 3 (1.3%) 9 (4.5%) 11 (4.3%) 5 (1.7%) 4 (1.3%)
    Worker's Comp 1 (0.5%) 3 (1.3%) 2 (1.0%) 0 (0%) 0 (0%) 4 (1.3%)
    Self-Pay 0 (0%) 0 (0%) 2 (1.0%) 0 (0%) 0 (0%) 1 (0.3%)
    Unknown 0 0 0 1 1 0
1 n (%)

Table of insurance companies

Show code
theme_gtsummary_compact()

db %>%
  transmute(
    "Rx year" = Fiscal.year,
    "Insurance company" = PAYOR_NAME,
    Insurance = FINANCIAL_CLASS
  ) %>%
  tbl_summary(
    by = "Rx year",
    missing = "ifany",
    sort = all_categorical() ~ "frequency",
    statistic = list(
      all_continuous() ~ "{median} ({p25}, {p75})",
      all_categorical() ~ "{n} ({p}%)"
    )
  )%>%
  bold_labels()
Characteristic 2018, N = 831 2019, N = 2221 2020, N = 2061 2021, N = 2231 2022, N = 2831 2023, N = 3211 2024, N = 1331
Insurance company






    HB/PB BDCT 26 (31%) 69 (31%) 56 (27%) 57 (26%) 55 (20%) 68 (21%) 46 (35%)
    MEDICARE 21 (25%) 50 (23%) 30 (15%) 50 (22%) 70 (25%) 65 (20%) 23 (17%)
    HB/PB URN - OPTUM HEALTH 3 (3.6%) 17 (7.7%) 20 (9.7%) 20 (9.0%) 16 (5.7%) 26 (8.1%) 7 (5.3%)
    MOLINA HEALTHCARE MEDICAID 5 (6.0%) 7 (3.2%) 18 (8.7%) 9 (4.0%) 18 (6.4%) 21 (6.6%) 4 (3.0%)
    AETNA US HEALTHCARE 4 (4.8%) 15 (6.8%) 5 (2.4%) 19 (8.5%) 13 (4.6%) 12 (3.8%) 8 (6.0%)
    PREMERA BLUE CROSS 7 (8.4%) 7 (3.2%) 7 (3.4%) 5 (2.2%) 15 (5.3%) 21 (6.6%) 4 (3.0%)
    HEALTH NET FEDERAL SERVICES TRICARE 0 (0%) 4 (1.8%) 5 (2.4%) 9 (4.0%) 7 (2.5%) 8 (2.5%) 0 (0%)
    WA MEDICAID 0 (0%) 6 (2.7%) 3 (1.5%) 5 (2.2%) 6 (2.1%) 7 (2.2%) 3 (2.3%)
    KAISER FHP OF WA (GROUP HEALTH) 5 (6.0%) 2 (0.9%) 5 (2.4%) 2 (0.9%) 2 (0.7%) 8 (2.5%) 4 (3.0%)
    BCBS OUT OF AREA 1 (1.2%) 1 (0.5%) 2 (1.0%) 5 (2.2%) 4 (1.4%) 8 (2.5%) 4 (3.0%)
    GENERIC/UNLISTED 1 (1.2%) 7 (3.2%) 8 (3.9%) 2 (0.9%) 0 (0%) 2 (0.6%) 0 (0%)
    HB/PB INTERLINK HEALTH SERVICES 1 (1.2%) 3 (1.4%) 2 (1.0%) 3 (1.3%) 3 (1.1%) 7 (2.2%) 0 (0%)
    COMMUNITY HEALTH PLAN OF WA MEDICAID 0 (0%) 3 (1.4%) 5 (2.4%) 1 (0.4%) 2 (0.7%) 4 (1.3%) 1 (0.8%)
    GRANTS AND BUDGETS 0 (0%) 2 (0.9%) 3 (1.5%) 6 (2.7%) 5 (1.8%) 0 (0%) 0 (0%)
    REGENCE BLUE SHIELD 0 (0%) 2 (0.9%) 4 (1.9%) 0 (0%) 3 (1.1%) 4 (1.3%) 3 (2.3%)
    COORDINATED CARE CORPORATION MEDICAID 1 (1.2%) 3 (1.4%) 1 (0.5%) 1 (0.4%) 4 (1.4%) 4 (1.3%) 1 (0.8%)
    OPTUM HEALTHCARE SOLUTIONS/URN 1 (1.2%) 1 (0.5%) 3 (1.5%) 0 (0%) 3 (1.1%) 4 (1.3%) 3 (2.3%)
    BCBS OF ILLINOIS - BOEING 0 (0%) 3 (1.4%) 2 (1.0%) 1 (0.4%) 2 (0.7%) 4 (1.3%) 2 (1.5%)
    WELLPOINT MEDICAID 0 (0%) 3 (1.4%) 5 (2.4%) 0 (0%) 2 (0.7%) 4 (1.3%) 0 (0%)
    ALASKA MEDICAID 1 (1.2%) 0 (0%) 0 (0%) 3 (1.3%) 2 (0.7%) 4 (1.3%) 1 (0.8%)
    CIGNA 0 (0%) 1 (0.5%) 2 (1.0%) 3 (1.3%) 3 (1.1%) 1 (0.3%) 1 (0.8%)
    TRIWEST HEALTHCARE ALLIANCE 0 (0%) 1 (0.5%) 1 (0.5%) 1 (0.4%) 1 (0.4%) 5 (1.6%) 2 (1.5%)
    HUMANA MEDICARE 0 (0%) 0 (0%) 1 (0.5%) 2 (0.9%) 5 (1.8%) 2 (0.6%) 0 (0%)
    AETNA MEDICARE 0 (0%) 1 (0.5%) 0 (0%) 1 (0.4%) 2 (0.7%) 1 (0.3%) 4 (3.0%)
    HB/PB LIFETRAC 0 (0%) 2 (0.9%) 1 (0.5%) 2 (0.9%) 3 (1.1%) 1 (0.3%) 0 (0%)
    REGENCE BLUE SHIELD MEDICARE 0 (0%) 0 (0%) 1 (0.5%) 1 (0.4%) 3 (1.1%) 1 (0.3%) 3 (2.3%)
    HEALTHCARE MGMT ADMIN 0 (0%) 1 (0.5%) 0 (0%) 1 (0.4%) 5 (1.8%) 0 (0%) 0 (0%)
    FHCC CONTRACTS 0 (0%) 0 (0%) 4 (1.9%) 0 (0%) 1 (0.4%) 1 (0.3%) 0 (0%)
    KAISER FHP OF WA MEDICARE (GROUP HEALTH) 0 (0%) 0 (0%) 4 (1.9%) 0 (0%) 0 (0%) 2 (0.6%) 0 (0%)
    MERITAIN HEALTH AETNA 1 (1.2%) 1 (0.5%) 0 (0%) 2 (0.9%) 1 (0.4%) 1 (0.3%) 0 (0%)
    MOLINA HEALTHCARE 0 (0%) 0 (0%) 0 (0%) 0 (0%) 4 (1.4%) 2 (0.6%) 0 (0%)
    MONTANA MEDICAID 0 (0%) 1 (0.5%) 0 (0%) 1 (0.4%) 0 (0%) 4 (1.3%) 0 (0%)
    REGENCE UMP 1 (1.2%) 0 (0%) 0 (0%) 1 (0.4%) 2 (0.7%) 2 (0.6%) 0 (0%)
    UNITED HEALTHCARE MEDICAID 0 (0%) 1 (0.5%) 1 (0.5%) 0 (0%) 1 (0.4%) 2 (0.6%) 1 (0.8%)
    ZENITH AMERICAN SOLUTIONS AETNA 0 (0%) 0 (0%) 1 (0.5%) 2 (0.9%) 2 (0.7%) 0 (0%) 1 (0.8%)
    BCBS OUT OF AREA MEDICARE 0 (0%) 0 (0%) 0 (0%) 2 (0.9%) 1 (0.4%) 2 (0.6%) 0 (0%)
    DEPT OF LABOR AND INDUSTRIES 0 (0%) 1 (0.5%) 1 (0.5%) 0 (0%) 0 (0%) 1 (0.3%) 1 (0.8%)
    FEP 0 (0%) 1 (0.5%) 0 (0%) 0 (0%) 2 (0.7%) 1 (0.3%) 0 (0%)
    KAISER FHP OF WA CORE-HIX (GROUP HEALTH) 1 (1.2%) 1 (0.5%) 1 (0.5%) 0 (0%) 0 (0%) 0 (0%) 1 (0.8%)
    PREMERA BLUE CROSS MEDICARE 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 3 (1.1%) 0 (0%) 0 (0%)
    US DEPARTMENT OF LABOR 1 (1.2%) 1 (0.5%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%) 1 (0.8%)
    FINANCIAL ASSISTANCE 0 (0%) 0 (0%) 1 (0.5%) 1 (0.4%) 0 (0%) 1 (0.3%) 0 (0%)
    MOLINA HEALTHCARE MEDICARE 0 (0%) 0 (0%) 1 (0.5%) 0 (0%) 1 (0.4%) 1 (0.3%) 0 (0%)
    OPTUM CARE NETWORK MEDICARE 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 1 (0.4%) 0 (0%) 1 (0.8%)
    ASURIS NORTHWEST HEALTH 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (0.6%) 0 (0%)
    BRITISH COLUMBIA 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 1 (0.3%) 0 (0%)
    FHCC HOLDING FUNDS 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 1 (0.3%) 0 (0%)
    IDAHO MEDICAID 0 (0%) 1 (0.5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.8%)
    INTERLINK 0 (0%) 0 (0%) 1 (0.5%) 0 (0%) 0 (0%) 0 (0%) 1 (0.8%)
    MEDICARE ADVANTAGE GENERIC 0 (0%) 1 (0.5%) 1 (0.5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    PACIFICSOURCE HEALTH PLANS 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 1 (0.3%) 0 (0%)
    UNITED HEALTHCARE 0 (0%) 1 (0.5%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%)
    BCBS OUT OF AREA GENERIC 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%) 0 (0%)
    BCBS OUT OF AREA MEDICAID 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%)
    COMMUNITY HEALTH PLAN OF WA MEDICARE 0 (0%) 1 (0.5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    FIRST CHOICE 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%)
    GEHA 1 (1.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    ILWU BENEFIT PLANS 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.8%)
    LIFEWISE HEALTH PLAN OF WA 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%)
    SELF INSURED WORKERS COMP OUT OF STATE 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%) 0 (0%)
    SELF INSURED WORKERS COMP WA 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%) 0 (0%)
    UNITED HEALTHCARE MEDICARE 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%)
    UNITED HEALTHCARE WEST MEDICARE 1 (1.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    WA STATE HEALTH INS POOL-BMI210 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%)
    WELLCARE OUT OF STATE MEDICARE 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.3%) 0 (0%)
    WELLPOINT MEDICARE 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0 (0%)
    Unknown 0 0 0 0 1 1 0
Insurance






    Case Rate 30 (36%) 91 (41%) 79 (38%) 82 (37%) 77 (27%) 102 (32%) 53 (40%)
    Medicare 22 (27%) 53 (24%) 38 (18%) 58 (26%) 88 (31%) 75 (23%) 31 (23%)
    Commercial 23 (28%) 44 (20%) 41 (20%) 44 (20%) 66 (23%) 75 (23%) 33 (25%)
    Medicaid 7 (8.4%) 25 (11%) 33 (16%) 20 (9.0%) 36 (13%) 50 (16%) 12 (9.0%)
    Other 0 (0%) 3 (1.4%) 8 (3.9%) 7 (3.1%) 8 (2.8%) 7 (2.2%) 2 (1.5%)
    Tricare 0 (0%) 4 (1.8%) 5 (2.4%) 9 (4.0%) 7 (2.5%) 8 (2.5%) 0 (0%)
    Worker's Comp 1 (1.2%) 2 (0.9%) 1 (0.5%) 2 (0.9%) 0 (0%) 2 (0.6%) 2 (1.5%)
    Self-Pay 0 (0%) 0 (0%) 1 (0.5%) 1 (0.4%) 0 (0%) 1 (0.3%) 0 (0%)
    Unknown 0 0 0 0 1 1 0
1 n (%)